Nearest Neighbor Algorithm Algorithm Principles The principle of the nearest neighbor method is to find a predefined number of training samples that are closest to the new point and predict the label from them. The number of samples can either be a user-defined constant (k-nearest neighbor learning) or can vary based on the local density … Read more
“Birds of a feather flock together” is a common phenomenon in real life, indicating that similar things are likely to have similar attributes. Using this idea, machine learning can classify data, assigning it to the same category, such as similar or “nearest” neighbors. Today, let’s learn about nearest neighbor classification. 1. Understanding Nearest Neighbor Classification … Read more
This article is an outstanding article from the Kexue Forum, Author ID: 大大薇薇 1 Overview python_mmdt: A Python library that generates feature vectors based on sensitive hashing (Part 1)We introduce a method called mmdthash (sensitive hashing) and provide a basic introduction to its concepts. python_mmdt: From 0 to 1 – Implementing a Simple Malicious Code … Read more
Today is the seventh issue summarizing 16 major topics and 124 interview questions in machine learning: K-Nearest Neighbors (KNN) algorithm interview questions. The K-Nearest Neighbors (KNN) algorithm works by finding the K nearest neighbors of a sample and using the information from these K neighbors to make predictions. For classification tasks, the majority voting method … Read more
This article will cover the essence of Softmax in terms of its principle and applications, helping you understand the Softmax function in one go. Softmax Activation Function 1. Essence of Softmax Essence Softmax is generally used as the last layer in a neural network for output in multi-class problems. Its essence is an activation function … Read more
Source fromMedium Author: garvitanand2 Compiled by: Machine Heart Contributors: Geek AI, Lu This article introduces the 10 most commonly used machine learning algorithms, including linear regression, Logistic regression, linear discriminant analysis, Naive Bayes, KNN, random forest, etc. 1. Linear Regression In the fields of statistics and machine learning, linear regression may be one of the … Read more
How to Obtain 1. Follow the public account below, and click 【Like】 and 【View】 in this article 2. Click 【Get Course】 in the public account to obtain this material There is a course on Basics of Machine Learning: Machine Learning and Materials/Chemistry Basics of Machine Learning: Machine Learning and Materials/Chemistry 1. Introduction to Machine Learning … Read more
In today’s digital age, machine learning is truly a hot topic. It acts like an intelligent assistant, helping us mine valuable information from vast amounts of data to make accurate predictions and decisions. With its simple and readable syntax and rich libraries, Python has become an excellent tool for implementing machine learning algorithms. Today, let’s … Read more
1. Introduction: Inferring from Clues Hello everyone! Imagine you are a detective investigating a case. You would infer who the suspect is based on various “clues” left at the scene (such as fingerprints, footprints, eyewitness descriptions, etc.). The Naive Bayes algorithm acts like a “probability detective”; it predicts the likelihood of an event occurring based … Read more
This article is a translated note of the Stanford University CS231N course, authorized for translation and publication by Professor Andrej Karpathy of Stanford University. This is a work from Big Data Digest, unauthorized reproduction is prohibited; specific requirements for reproduction can be found at the end of the article. Translation: Han Xiaoyang & Long Xincheng … Read more